Introduction

At Granum Technologies LLC, we’re continually exploring innovative ways to empower financial institutions with data-driven insights. Today, we’re excited to introduce our new Vintage Analysis Tool, designed to transform the way you assess loan performance over time. This tool categorizes loans into distinct "vintages" based on their boarding dates and tracks critical performance metrics—such as cumulative charge-offs relative to the original amount financed—over successive periods.

As the first offering in our free Open Finance Library series, the Vintage Analysis Tool lays the groundwork for a suite of analytical tools aimed at unraveling the complexities of modern finance. We’re also eager to hear your feedback and suggestions for additional tools that can further enhance your financial analysis.

Key Features and Capabilities

1. Comprehensive Loan Grouping

Vintage Categorization: Group loans based on their boarding dates, whether on a monthly, quarterly, or yearly basis.

Aging Analysis: Evaluate loan performance over time using a customizable aging period (monthly, quarterly, or yearly).

2. In-Depth Data Analysis

Data Validation: Robust checks ensure that your input data is accurate and complete, flagging issues like missing critical dates or negative values.

Calculation Flexibility: Choose between calculating cumulative net call-offs as a total sum or as a percentage of the original financing, ensuring the analysis meets your specific needs.

3. Visual and Exportable Insights

Detailed Matrix Output: Generate a vintage matrix that captures the evolution of loan performance across different cohorts.

Dynamic Graphs: Create clear, intuitive line graphs that visualize cumulative charge-offs over time, helping you quickly identify trends and potential risks.

Seamless File Integration: Supports both CSV and Excel formats, with outputs that can be saved and shared easily.

How it Works

Step 1: Open our Google Colab Notebook

Step 2: Run all Cells and Upload Your Data

Run all cells by going to the 'Runtime' tab then 'Run All' (Ctrl + F9 in chrome). The cells will run until the portion where it is waiting on you to upload your file. You can upload your file in CSV or XLSX format with any name. Please be sure that the file is in the correct format which is detailed in the Colab Notebook and our GitHub.

Step 3: Set Your Variables

Customize your analysis by setting key variables:

Vintage Period Type: Choose how loans are grouped (monthly, quarterly, or yearly).

Aging Period Type: Decide the frequency for aging calculations (monthly, quarterly, or yearly).

Calculation Type: ‘sum’ of charge-offs or a ‘percent’ calculation relative to original amounts financed.

Step 4: Data Validation and Processing

The tool verifies your data’s integrity by:

Converting date fields and numerical values correctly.

Checking for common data quality issues such as missing values or invalid date sequences.

Grouping loans into vintages and calculating cumulative performance metrics.

Step 5: Generate Outputs

Once processed, the tool produces:

An Excel Matrix: A detailed file showcasing the vintage analysis, available for further review and reporting.

A Visual Plot: A PNG image that graphically represents the cumulative performance of each vintage, highlighting trends and potential areas of concern.

Why Vintage Analysis?

Understanding loan performance over time is critical in today’s dynamic financial landscape. Vintage analysis helps institutions:

Assess Risk: Identify cohorts that may be underperforming, allowing for proactive risk management.

Monitor Trends: Track how different vintages evolve, offering insights into market conditions and loan quality.

Inform Strategy: Use historical data trends to guide future lending practices and portfolio management.

The Open Finance Library Series

The Vintage Analysis Tool is just the beginning. With the Open Finance Library, we aim to build a comprehensive toolkit for financial data analysis. Future tools under this series may include:

Credit Score Evolution Analysis: Dive deeper into how credit scores change over time.

Portfolio Diversification Metrics: Assess and optimize the diversity of loan portfolios.

Predictive Analytics Tools: Leverage machine learning to forecast loan performance and default risks.

We are open to your suggestions and ideas. What other financial analysis challenges are you facing? Let us know how we can tailor our upcoming tools to better serve your needs.

For further details, an in-depth guide, and example files, please visit our official GitHub Repository or reach out to us at info@granum-tech.com.